Cargando…
A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia
BACKGROUND: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. METHODS: The A...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5222522/ https://www.ncbi.nlm.nih.gov/pubmed/28027323 http://dx.doi.org/10.1371/journal.pntd.0005252 |
_version_ | 1782493033039659008 |
---|---|
author | Alderton, Simon Macleod, Ewan T. Anderson, Neil E. Schaten, Kathrin Kuleszo, Joanna Simuunza, Martin Welburn, Susan C. Atkinson, Peter M. |
author_facet | Alderton, Simon Macleod, Ewan T. Anderson, Neil E. Schaten, Kathrin Kuleszo, Joanna Simuunza, Martin Welburn, Susan C. Atkinson, Peter M. |
author_sort | Alderton, Simon |
collection | PubMed |
description | BACKGROUND: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. METHODS: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation. RESULTS: Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5–10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies. CONCLUSION: ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale. |
format | Online Article Text |
id | pubmed-5222522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-52225222017-01-19 A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia Alderton, Simon Macleod, Ewan T. Anderson, Neil E. Schaten, Kathrin Kuleszo, Joanna Simuunza, Martin Welburn, Susan C. Atkinson, Peter M. PLoS Negl Trop Dis Research Article BACKGROUND: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. METHODS: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation. RESULTS: Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5–10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies. CONCLUSION: ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale. Public Library of Science 2016-12-27 /pmc/articles/PMC5222522/ /pubmed/28027323 http://dx.doi.org/10.1371/journal.pntd.0005252 Text en © 2016 Alderton et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alderton, Simon Macleod, Ewan T. Anderson, Neil E. Schaten, Kathrin Kuleszo, Joanna Simuunza, Martin Welburn, Susan C. Atkinson, Peter M. A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia |
title | A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia |
title_full | A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia |
title_fullStr | A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia |
title_full_unstemmed | A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia |
title_short | A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia |
title_sort | multi-host agent-based model for a zoonotic, vector-borne disease. a case study on trypanosomiasis in eastern province, zambia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5222522/ https://www.ncbi.nlm.nih.gov/pubmed/28027323 http://dx.doi.org/10.1371/journal.pntd.0005252 |
work_keys_str_mv | AT aldertonsimon amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT macleodewant amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT andersonneile amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT schatenkathrin amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT kuleszojoanna amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT simuunzamartin amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT welburnsusanc amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT atkinsonpeterm amultihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT aldertonsimon multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT macleodewant multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT andersonneile multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT schatenkathrin multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT kuleszojoanna multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT simuunzamartin multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT welburnsusanc multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia AT atkinsonpeterm multihostagentbasedmodelforazoonoticvectorbornediseaseacasestudyontrypanosomiasisineasternprovincezambia |